MemMachine vs OpenViking
Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations. — versus — Volcengine's context database: memories, resources and skills as one `viking://` filesystem agents ls, tree and grep — L0/L1/L2 tiers, traceable retrieval, sessions distilled into memory.
Both are long-term memory backends for agents; MemMachine exposes episodic, profile and working memory through SDKs, OpenViking makes the entire context a browsable namespace with tiered summaries. Pick by whether you want an API or a filesystem agents explore.
| MemMachine | OpenViking | |
|---|---|---|
| Stars | 3.2k | 32k |
| Forks | 205 | 2.4k |
| Language | Python | Python |
| License | Apache-2.0 | AGPL-3.0 |
| Last activity | 4 days ago | yesterday |
| Topics | memory | memory, rag, skills |
| Curated connections | 12 | 5 |
MemMachine — the curator's take
Pick it when memory is a product requirement, not a cache: separating episodic (graph) from profile (SQL) from working memory maps to how assistants actually personalize, and the documented LangGraph/CrewAI/LlamaIndex integrations mean you don't write the glue. NOT worth the footprint for a single-user tool — it wants a server plus Neo4j and SQL; a vector store or a JSON file gets a prototype further. Watch the open-core boundary: the managed platform is the business model.
OpenViking — the curator's take
Use it when opaque vector recall has burned you: every query keeps the directory-browsing trajectory that produced it, so a wrong answer is debuggable, and the L0/L1/L2 tiers let an agent judge relevance before paying for full content. It pays off most for long-lived agents with heavy reference material — repos, docs, per-user preferences — and there are documented hooks for Claude Code, Codex, Cursor, OpenCode, LangChain/LangGraph and plain MCP clients. Costs: it's a server plus semantic pre-processing on every write, so ingest is slow and not free; AGPLv3 rules it out of many closed products; and there's an obvious managed-SaaS path on Volcano Engine behind it. Wrong tool if you just want an in-process vector index.